The current approach to AI visualizations relies on base iterations, meaning it builds from pre-existing models, concepts, or datasets. While this method is useful, it limits the AI’s ability to truly create or validate novel ideas. To solve this, I propose the integration of a sandbox environment where AI can build visualizations from scratch using applied physics, material properties, and other relevant factors.
The Problem:
When AI generates a visualization, it pulls from existing images or concepts. This creates a situation where visualizations are not entirely novel, often resembling or correlating with known designs. While the concepts being visualized may be sound, they lack originality because the AI doesn’t have the tools to fully innovate. For example, if someone wants to use AI to validate a new machine concept or a quantum idea through visualization, the AI simply can’t do it reliably.
The Solution:
The answer is to provide AI with a sandbox environment, like those found in programs such as Blender or Creo. These environments allow for 3D modeling, material property integration, and realistic physics simulations. With these tools, the AI could:
- Build From Scratch: Instead of pulling from known concepts, the AI could construct entirely new systems and structures.
- Simulate Physics: By incorporating physics engines, the AI could validate concepts by simulating real-world interactions, environmental effects, or material behaviors.
- Produce Accurate Outputs: Once the AI has built and simulated a model, it can generate 2D images or single frames from the 3D environment. These outputs would reflect a more thorough and accurate representation of the idea.
Why Blender and Creo?
Programs like Blender and Creo are industry-standard tools for 3D modeling and engineering simulations. Creo, for instance, offers advanced material property integration and physics elements, making it ideal for this purpose. However, it comes with a high cost—between $1,500 and $10,000 annually for a license. Blender, being open-source, might be a more accessible starting point, though it would need adaptation for this specific use case.
The Vision:
Imagine an AI that doesn’t just explain ideas or produce static visuals but actively builds and validates concepts in a dynamic, interactive environment. This would move AI from being a conceptual assistant to an engineering partner, capable of bridging the gap between imagination and physical application. This isn’t just about improving visuals; it’s about creating tools that can simulate and test ideas before they’re built, saving time and resources.
Call to Action:
To make this a reality, I propose developing AI systems that can integrate with sandbox environments like Blender or Creo. If successful, this would revolutionize how AI is used in design, engineering, and scientific validation. It’s an investment in the future of innovation, where AI not only interprets the world but helps build it.